I-Corps: Translation Potential of a Handheld Standoff Photothermal Spectroscopy System for Real-time Indication of Viral Epidemics
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2449371
Grant search
Key facts
Disease
COVID-19, UnspecifiedStart & end year
20252026Known Financial Commitments (USD)
$50,000Funder
National Science Foundation (NSF)Principal Investigator
Thomas ThundatResearch Location
United States of AmericaLead Research Institution
SUNY at BuffaloResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Diagnostics
Special Interest Tags
N/A
Study Type
Clinical
Clinical Trial Details
Not applicable
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
This I-Corps project is focused on the development of an innovative. non-invasive, diagnostic tool for viral infections. The technology is able to provide rapid, accurate, and real-time detection of influenza, respiratory syncytial virus, and COVID-19. The tool's portability ensures that it can be widely distributed, making it accessible in a variety of healthcare settings, including clinics, hospitals, and in remote areas with limited medical infrastructure. By enabling early and precise diagnosis, this tool can improve patient outcomes, reduce the spread of infectious diseases, and alleviate the burden on healthcare systems. Furthermore, its scalability and low manufacturing costs position it as a viable option for mass production and global distribution, addressing urgent public health needs. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a method to determine multiple pathological conditions simultaneously. The solution detects the infrared signatures produced by the resonant excitation of certain molecules using a tunable source. This approach achieves a limit of detection that is orders of magnitude higher than available nanosensors. By applying machine learning techniques to analyze the nanomechanical infrared response profile, multiple pathological conditions can be identified simultaneously. This device is capable of continuous miniaturization, making it portable and affordable for widespread deployment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.